WO2016197648A1 - Procédé de détection et de reconnaissance d'action basé sur un signal sans fil - Google Patents

Procédé de détection et de reconnaissance d'action basé sur un signal sans fil Download PDF

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Publication number
WO2016197648A1
WO2016197648A1 PCT/CN2016/076575 CN2016076575W WO2016197648A1 WO 2016197648 A1 WO2016197648 A1 WO 2016197648A1 CN 2016076575 W CN2016076575 W CN 2016076575W WO 2016197648 A1 WO2016197648 A1 WO 2016197648A1
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wireless
signal
data
motion
signal strength
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PCT/CN2016/076575
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Chinese (zh)
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刘向阳
王玮
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南京大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Definitions

  • the present invention relates to the field of wireless networks and pervasive computing, and more particularly to a method for motion detection and recognition based on wireless signals.
  • Human motion detection and recognition technology is a core technology of ubiquitous computing. With the rapid development of industries such as wearable computing and smart home, in recent years, applications based on various behavior recognition technologies have shown a situation of spurt development. For example, motion monitoring and recording technology based on mobile phones and wristbands can help consumers understand their own sports and sleep behaviors, thereby actively changing their lifestyle. The elderly care system with human motion monitoring function can provide applications such as fall alarms and life routine detection, which are very important for elderly care. In the intelligent security system, behavior recognition technology can also be used to determine whether abnormal behavior occurs in the monitored area.
  • wireless access devices have spread to thousands of households and various public places. Since the human body is a good conductor of electricity, people have a strong reflection effect on radio waves. In this way, wireless devices that can be seen everywhere around us can actually play the role of "human radar.”
  • the key advantages of motion detection and recognition technology based on wireless signals are: First, the monitored person does not need to wear any equipment, so the non-active matching target can be monitored; in addition, the system only needs to use existing common wireless devices, such as notebooks. , mobile phones, WiFi routers, etc., software upgrades can be achieved, the cost is very low.
  • the existing human motion detection system based on the universal wireless device has weak performance and can only recognize one or two actions, and the recognition effect is affected by the surrounding environment.
  • CN103606248A He proposed to use support vector machine and anomaly detection technology to achieve the judgment of falling.
  • the limitation is that the system can only recognize the action of falling, and the system detection distance is limited.
  • the present invention has been made to improve the above technical drawbacks.
  • Excessive noise in the measurement results of general-purpose wireless devices is a fundamental problem that limits the effectiveness of motion detection and identification.
  • the invention proposes a method of principal component analysis to jointly process multi-channel wireless signal data, and proposes components related to human motion, thereby achieving the effect of eliminating noise.
  • the present invention acquires the speed characteristics of the motion by the influence of human motion on the fluctuation of the wireless signal, and performs motion recognition. This method reduces the impact of the environment on the signal, so that it can obtain better recognition results in different environments.
  • the object of the present invention is to propose a motion detection and motion recognition method based on wireless signals, aiming at the weakness of the existing wireless signal motion recognition technology, solving how to obtain a reliable wireless signal through a universal wireless device, and achieving a comparison in different environments. High recognition efficiency issues.
  • the present invention is implemented by the following technical solution: a motion detection and motion recognition method based on wireless signals, characterized in that wireless signal data is collected by using one or more general wireless devices, and multiple wireless signals are transmitted. Correlation of data Denoising the wireless data, and extracting the characteristics related to the speed of the human body from the wireless data to detect and identify the motion, including the following basic steps:
  • Data acquisition by measuring the received wireless signal, including measuring the RSSI and CSI of each data packet;
  • PCA Principal component analysis
  • OFDM Orthogonal Frequency Division Multiplexing
  • Motion feature extraction that is, using time-frequency analysis of wireless signal data to obtain the intensity of the wireless signal at different frequencies, and the time-frequency analysis method includes Short-Time Fourier Transform (STFT) and Wavelet Transform (Wavelet Transform);
  • STFT Short-Time Fourier Transform
  • Wavelet Transform Wavelet Transform
  • Model training that is, the training of the offline data set by the system, the training data set is established by collecting the wireless signals corresponding to different actions, and the signals are manually labeled and segmented during the collection process, and the labeling process indicates which specific signals belong to which An action, the segmentation is manually marking the start and end points of the action, and for each action, collecting signals of different people in different environments to form a training data set, for the characteristics of an action instance, adopting
  • the method including vector quantization first quantizes the signal strength of each frame or directly uses a mixed Gaussian hidden Markov model for training;
  • Motion recognition and motion recognition method adopt Hidden Markov Model (HMM), which inputs each frame signal strength vector of motion feature extraction into multiple hidden Markov models, and each hidden Markov model corresponds to one action. For each hidden Markov model, the system calculates the probability of generating a current signal strength vector sequence, and the system selects the action corresponding to the model with the largest possible generation of the current signal vector sequence as the recognition result.
  • HMM Hidden Markov Model
  • the universal wireless device refers to a wireless device supporting WiFi, Long Term Evolution (LTE), Bluetooth, and Zigbee communication technologies.
  • the wireless signal data includes a Received Signal Strength Indication (RSSI) and a Channel State Indication (CSI).
  • RSSI Received Signal Strength Indication
  • CSI Channel State Indication
  • the beneficial effects of the present invention are as follows: aiming at the defects that the existing human motion recognition technology cannot be implemented on the general wireless device, a method for performing noise reduction processing on the wireless signal data by using multiple signals is proposed, and the action is performed by using a relatively stable motion speed feature. Identify.
  • the benefit is that motion recognition can be implemented on existing wireless devices with a simple software upgrade.
  • the system can achieve better recognition results in different types of environments, such as indoor line of sight, indoor no line of sight and outdoor.
  • the anti-jamming capability of the wireless multipath is also stronger by utilizing the characteristics of the motion speed.
  • Figure 1 is an application scenario of the present invention.
  • Figure 2 is a flow chart showing an embodiment of the present invention.
  • Figure 3 is an embodiment of a denoising process.
  • 4 is a diagram showing an example of collected wireless signal strength raw data.
  • FIG. 5 is a diagram showing an example of wireless signal strength after denoising processing.
  • Figure 6 is a diagram showing an example of signal intensity variation caused by walking.
  • Fig. 7 is a diagram showing an example of changes in signal intensity caused by sitting down.
  • Fig. 8 is a view showing an example of a change in signal intensity caused by a fall.
  • Fig. 9 is a diagram showing an example of a feature acquired from a fall signal.
  • the invention utilizes the interference of the human body motion on the wireless signal, uses the universal wireless device to collect the wireless signal data, denoises the data, and extracts the feature associated with the motion speed to identify the action.
  • Collecting wireless signal data using one or more general-purpose wireless devices denoising wireless data through correlation of multiple wireless signal data, and extracting features related to human motion speed from wireless data to detect and identify motion .
  • the main technical features are: general wireless device; second, denoising processing by correlation; third, moving speed characteristics.
  • the basic steps include: data acquisition, data denoising, data segmentation, feature extraction, model training, and motion recognition.
  • the denoising process performs principal component analysis (PCA) on the multiplex signal to obtain the data component with the lowest noise.
  • PCA principal component analysis
  • the multiplex signal refers to measurement data on different subcarriers in Orthogonal Frequency Division Multiplexing (OFDM), and/or measurement data of different transmit/receive antennas, and/or different Measurement data between devices.
  • OFDM Orthogonal Frequency Division Multiplexing
  • the universal wireless device refers to a wireless device supporting WiFi, Long Term Evolution (LTE), Bluetooth, and Zigbee communication technologies.
  • the wireless signal data refers to a Received Signal Strength Indication (RSSI) and/or a Channel State Indication (CSI).
  • RSSI Received Signal Strength Indication
  • CSI Channel State Indication
  • the motion feature extraction uses time-frequency analysis of wireless signal data to obtain wireless The strength of the signal at different frequencies.
  • time-frequency analysis refers to a short-time Fourier transform (STFT) and/or a wavelet transform (Wavelet transform).
  • STFT short-time Fourier transform
  • Wavelet transform wavelet transform
  • the motion recognition method adopts a Hidden Markov Model (HMM).
  • HMM Hidden Markov Model
  • FIG. 1 is an embodiment of the present invention.
  • One or more wireless transmitters 101 are included in the scene.
  • the wireless transmitter 101 can employ WiFi, LTE, Bluetooth or ZigBee technology, and the signal transmitted by the wireless transmitter 101 is a normal data message conforming to the corresponding wireless technology protocol.
  • One or more wireless receivers 102 are included in the scene.
  • the wireless receiver 102 receives the signals of the wireless transmitter.
  • the wireless receiver 102 can measure the strength of the wireless signal transmitted by the wireless transmitter 101 by RSSI or CSI.
  • RSSI or CSI As shown in FIG. 1, from the wireless transmitter 101 to the wireless receiver 102, radio waves can be reached through two different paths.
  • the line-of-sight (LOS) path 104 is a direct path, and the reflection path 105 is reflected by the moving body 103 to reach the wireless receiver 102.
  • LOS line-of-sight
  • the reflection path 105 is constantly changing, thereby causing a change in the signal strength received by the wireless receiver 102.
  • the present invention detects and recognizes human motion by the intensity changes received by the wireless receiver 102.
  • the invention is characterized in that the object 103 to be detected does not need to be worn with any special equipment or sensors, and its action can be recognized completely by reflection from the human body.
  • the wireless transmitter 101 and the wireless receiver 102 may be general-purpose electronic devices including, but not limited to, mobile phones, wireless routers, cellular base stations, notebook computers, smart set top boxes, wireless sensors, smart wearable devices, and the like.
  • the above general electronic device only needs to have a wireless signal transceiver to be used in the present invention, and no special hardware modification is required.
  • wireless signal data acquisition 201 is first performed, and then the data is subjected to denoising processing 202.
  • the denoised processed data can be used for offline model generation or for online motion recognition.
  • the motion is first detected and segmented 203, then the feature extraction 204 is performed, and finally the model-based motion recognition 206 is performed using the extracted features in combination with the motion model.
  • the model of the comparison in step 206 is obtained by offline model training 205. It is worth noting that the above process is only one possible implementation of the present invention, human motion recognition and detection.
  • the measurement system can also be implemented in a variety of other ways.
  • the wireless signal data acquisition employed by the present invention is implemented by the wireless receiver 102.
  • the wireless receiver 102 performs intensity detection on the signals of the wireless transmitter 101. This can be accomplished by the wireless transmitter 101 transmitting data at a particular rate, such as 2500 packets per second, while the wireless receiver 102 performs intensity measurements on each data packet. In addition, measurements can also be made by intensity detection of the daily data flow of the wireless transmitter 101.
  • the specific data acquisition method can be implemented by measuring the RSSI or CSI of each data packet. Most popular wireless devices currently support wireless measurement data via RSSI or CSI. Of course, we do not exclude the wireless signal receiver 102 from measuring the received wireless signal by other means.
  • Wireless signal measurements can be multiplexed.
  • the multipath here refers to the following situations: First, when the wireless signal adopts OFDM modulation, the signal strength on multiple subcarriers can be measured separately; second, when the wireless transmitter 101 or the wireless receiver 102 has multiple antennas, each The signal strength on the transmit/receive antenna pair can be measured separately; third, when there are multiple wireless transmitters or multiple wireless receivers, the signal strength on each pair of transmitters/receivers can be measured separately. These individually measured wireless data can be viewed as separate multiplexed signals.
  • the invention proposes a joint denoising algorithm for multi-path signals, which is mainly based on the correlation of human motion in multi-path signals. Using this correlation, principal component analysis can be used to extract motion-related information from multiple signals.
  • FIG. 3 illustrates a specific method using principal component analysis by taking multiple signals on multiple subcarriers in OFDM as an example.
  • the wireless receiver 102 can measure a series of signal strengths on each subcarrier. We first arrange the signal strengths 301 on different subcarriers in order of time and subcarriers. We then preprocess the sequence of signal strengths on a single subcarrier, which subtracts the long-term average of the signal strength from the signal strength time series to obtain the pre-processed signal strength 302.
  • the pre-processed signal strength is segmented to obtain a measurement data matrix H.
  • Each row of the measurement matrix H represents the signal strength on a single subcarrier, at the same time on each column The measured signal strength on different subcarriers.
  • the number n of rows of the measurement matrix H is equal to the number of subcarriers, and the number of columns m of the measurement matrix is equal to the length of the time series.
  • the dimension of the measurement matrix H is 30 x 2500.
  • the dimension of the correlation matrix 303 is n x n.
  • the feature vector 304 is arranged according to the size of the feature value, and is represented as q 1 , q 2 , . . . , q n . Each feature vector has a length of n.
  • the measurement matrix H is multiplied by the feature vector 304 to obtain the individual PCA components of the signal.
  • PCA component 305 is a relinear combination of multiplexed signals. Therefore, the noise in each signal is weakened due to its uncorrelated characteristics. On the other hand, the interrelated human motion information in each signal will be enhanced in the first few components of the PCA. Normally, we take the second component of PCA as a result of denoising.
  • Figure 4 and Figure 5 plot the original acquired CSI signal strength and signal strength after denoising. We can see that the signal noise after processing is greatly reduced. At the same time, the high frequency components of the signal are still preserved.
  • the online recognition system After denoising, the online recognition system first needs to detect and segment the action.
  • the variance information of the signal can be utilized to determine whether there is an action.
  • the wireless signal strength will fluctuate significantly, and the judgment can be made by increasing the variance.
  • the determination may be made, for example, by the smoothness of the feature vector of the PCA described above.
  • the feature vector tends to be smooth, the correlation of each signal is enhanced, and an action may occur at this time.
  • the action can be split.
  • specific actions are identified based on the segmented action information.
  • the specific segmentation method can use a fixed time segmentation, such as cutting into a segment every 3 seconds. Or split according to the start and end time of the action.
  • the present invention employs features related to the speed of movement of the human body for motion recognition.
  • the speed characteristics of different movements of the human body are different. For example, when walking, the overall movement speed of the human body is about 1 m / s, while the running speed can reach 2-3 m / sec. In addition to this, the speed of the movement changes regularly. For example, when a person falls, there is a process of accelerating the free fall, and the speed of movement is significantly accelerated. Therefore, the movement speed information can be used to identify various actions of the person, including: walking, running, brushing, pushing hands, standing up/sitting, falling, opening/closing, boxing, and the like.
  • the change of wireless signal strength is affected by human motion, and the frequency of change is related to the speed of human motion.
  • a wireless signal having a carrier frequency of 5 GHz has a wavelength of 6 cm.
  • the wireless signal strength will vary at a frequency of 33 Hz corresponding to the motion speed of 1 m/s.
  • Figures 6, 7, and 8 illustrate examples of changes in wireless signal strength caused by walking, sitting, and falling, respectively. It can be clearly seen from the figure that the speed of walking is higher than that of sitting, and in the process of falling, there is a signal frequency that is accelerated, that is, the process of motion acceleration.
  • the intensity variation frequency of the wireless signal can be obtained by various time-frequency analysis methods.
  • Common time-frequency analysis methods include short-time Fourier transform and wavelet transform.
  • the short-time Fourier transform divides the signal into frames by means of windowing, calculates the Fourier transform value of each frame signal, and obtains the strength of the signal at different frequencies.
  • each frame of the signal will produce an intensity vector, each of which represents the strength of the signal at a certain frequency in the frame.
  • a typical frame length can take 512 or 1024 sample points, and 32 or 64 sample points can be moved between frames. This allows you to get the difference in signal strength over time and frequency.
  • the wavelet transform can also be used to obtain the signal strength vector of each frame signal in each frequency band.
  • Figure 9 is an example of features acquired from a fall signal.
  • the abscissa is time, the ordinate is frequency, and the brightness of the square represents the energy of the signal.
  • the signal energy is concentrated on The low frequency part indicates that the movement speed is low.
  • the time-frequency energy characteristics of the signal can be extracted by a short-time Fourier transform or a wavelet transform. Using this energy feature, pattern recognition can be used to identify specific actions.
  • One such implementation is to use a hidden Markov model for identification.
  • the hidden Markov model is widely used in speech signal recognition, and its application in wireless signal motion recognition is similar to that in speech signal recognition.
  • the specific implementation manner may establish a training data set by collecting wireless signals corresponding to different actions. Signals can be manually labeled and segmented during the acquisition process. The labeling process is to indicate which action a particular signal belongs to. Segmentation is the manual marking of the start and end points of the action. For each action, a plurality of different people can be collected, and signals of actions are performed in different place environments to form a training data set. For each specific action in the training data set, such as walking, the feature extraction method described above can be used to acquire features.
  • the vector quantization method can be used to quantize the signal strength of each frame, or directly by using the mixed Gaussian hidden Markov model.
  • the traditional expectation maximization algorithm can be used to iteratively generate the action model.
  • Offline training generates a corresponding hidden Markov model for each specific action. This model can be used for online motion recognition.
  • the online recognition system After performing motion detection and feature extraction, the online recognition system inputs the extracted frame signal strength vectors into multiple hidden Markov models. Each hidden Markov model corresponds to an action. For each Hidden Markov Model, the system calculates its likelihood of generating a current signal strength vector sequence. The system selects an action corresponding to the model having the largest possible generation of the current signal vector sequence as the recognition result.

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Abstract

L'invention concerne un procédé de détection et de reconnaissance d'action basé sur un signal sans fil qui se rapporte aux domaines des réseaux sans fil et de l'informatique omniprésente, en particulier un procédé de détection et de reconnaissance d'action basé sur un signal sans fil. Le procédé utilise une interférence d'un mouvement de corps humain sur un signal sans fil, adopte un dispositif sans fil d'usage général pour acquérir des données de signal sans fil (201), et extrait une caractéristique associée à la vitesse du mouvement après avoir appliqué un traitement de débruitage aux données, de manière à reconnaître le mouvement. Le procédé consiste à : utiliser un ou plusieurs dispositifs sans fil à usage général pour acquérir les données de signal sans fil (201), appliquer le traitement de débruitage (202) aux données sans fil au moyen de la corrélation de multiples canaux de données de signal sans fil, et extraire les caractéristiques associées à la vitesse du mouvement de corps humain des données sans fil de manière à détecter et à reconnaître le mouvement. Les étapes de base comprennent : l'acquisition de données (201), le débruitage de données (202), une segmentation de données (203), l'extraction de caractéristiques (204), un apprentissage de modèle (205), et la reconnaissance de mouvement (206).
PCT/CN2016/076575 2015-06-10 2016-03-17 Procédé de détection et de reconnaissance d'action basé sur un signal sans fil WO2016197648A1 (fr)

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